TL;DR:

Everyone is chasing AI automation — but before any of that matters, adoption must be earned.

The AI Accuracy Score isn’t just another metric. It quantifies how deeply AI is integrated into the agent workflow — showing where trust is built, where friction remains, and what needs to evolve before scale can happen. It’s the operational pulse check for real-world AI adoption in CX.
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Why Adoption, Not Automation, Is the Real Battleground

Automation has become the dominant theme in conversations about AI and customer experience.

Smart bots, large language models, asynchronous resolution flows, and AI copilots have captured the attention of CX, BPO, and technology leaders. In many organizations, these tools are already deployed at scale.

Yet despite their availability, real adoption remains limited.

The challenge is not access to AI. It is not model quality or deployment speed. The true barrier lies in integration — not just at the systems level, but in how AI is used by people.

Support agents often bypass AI-generated responses, revert to existing macros, or rewrite suggestions entirely. Even when the AI output is technically accurate, it frequently fails to align with how agents work in real time. In these situations, AI becomes peripheral — technically present, but practically unused.
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This is not a failure of intelligence. It is a failure of trust, usability, and workflow fit.

And it exposes a fundamental misalignment: while most organizations measure AI adoption through system-level metrics — feature activation, latency, or response rate — the real indicator of maturity lies in human behavior.

Most teams can report how often AI is deployed. Few can say whether it’s genuinely shaping the way agents work.

To close that gap, organizations need a different kind of signal — one that reflects not just the presence of AI, but its adoption, usability, and accuracy in real workflows.
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That’s exactly what the AI Accuracy Score was built to do.

It’s a behavioral metric that captures how deeply AI is integrated into agent workflows — and how much of its output is actually trusted and used.

In doing so, it provides what most CX teams are missing: a practical, measurable way to track where AI is creating value — and where it’s falling short.

About the AI Accuracy Score

What is it?

The AI Accuracy Score is a new operational metric designed to answer a simple but critical question:
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Is the AI being used — or is it being ignored?

It quantifies how much AI-generated content is actually being used — and trusted — by agents in live customer interactions.

Rather than measuring how often a feature is accessed or how accurate a model is in isolation, the AI Trust Score focuses on real-world adoption: how often the AI output is accepted, edited, or rejected in daily workflows.

To calculate this, we use a method called “Levenshtein distance” — a way to compare two pieces of text by counting how many edits are needed to turn one into the other.

• Fewer edits = higher similarity = higher trust.
• More edits = lower similarity = lower trust.
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Levenshtein distance measures how many changes are made between the AI’s message and the agent’s final version. The fewer the edits, the higher the score — signaling stronger trust and smoother integration:

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Figure 1: The words “KITTEN” and “SITTING” require three edits to match — this is what Levenshtein distance measures. The more edits, the lower the score.

How the Score Works?

Every time AI suggests a message, the system compares it to what the agent actually sends to the customer.

• If the final message is nearly identical → High score
• If the agent rewrites or ignores the suggestion → Low score

The more similar the two messages are, the higher the score — a direct signal of trust, usability, and workflow fit.

Here’s how the process works:

1. A customer sends a message
2. The AI generates a suggested reply
3. The AI-generated reply is presented to the agent
4. The agent reviews, edits (if needed), and sends the final response
5. The AI Accuracy Score is calculated instantly after the message is sent

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The formula:

To calculate the score, the system compares the AI-generated message with the final message using this equation: It counts the number of edits (insertions, deletions, or substitutions) required to turn one string into another.

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This produces a percentage between 0 and 1 that reflects how closely the agent’s message matches the AI’s suggestion.

• A score near 1.0 = high similarity = High trust
• A score near 0 = full rewrite = Low trust

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The design:

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This scoring system was designed through extensive observation of agent-AI interaction patterns across real workflows. Thresholds were based on how much agents typically altered AI-generated responses — and what those changes meant in context.

We found that scores above 60% often meant the agent only made light edits — trimming a sentence here, tightening phrasing there. But when scores dropped below 30%, it usually meant the AI was being rewritten entirely. It’s not just about correctness — it’s about how much the agent actually relies on what the AI produced,” said Andrew Pierce, Head of Product at XO.

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Each time the AI system suggests a reply, the agent can:

• Use it as-is
• Make a few small edits
• Or rewrite it entirely —  ignore it altogether.

The AI Accuracy Score captures these decisions and turns them into a measurable signal.

This method captures a critical distinction:

An AI model may perform well technically, but unless it is used and trusted, it won’t deliver impact at scale.

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Figure 3: These real message comparisons show how the score is reflected in practice. In each example, the agent kept most of the AI’s message, with light edits — resulting in a high accuracy score.
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These examples help ground the metric in reality. You can see where the AI is helping — and where it’s not.

Over time, this becomes more than a number. It becomes a lens into how well your AI is actually being adopted across different teams, workflows, and customer scenarios.

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What Companies Can Actually Do With This Score

The AI Accuracy Score isn’t just a metric for adoption.

It’s one part of a larger picture we’re building to understand how AI performs in real-world use.

As we develop additional metrics — like operational impact and semantic match — this score acts as a starting point. It gives teams a clear signal: is the AI being used, and is it trusted?

Over time, it’ll sit alongside other measures to give a more complete view of AI adoption and effectiveness. But even on its own, it’s a powerful lens into how AI is performing inside real workflows — at scale.

When adoption is low, it’s tempting to blame the model.

But this score helps teams look deeper — into the behavior patterns that drive trust, friction, or rejection.

It reveals patterns that help leaders act faster and smarter, unlocking operational leverage:

• AI Operations (the AI Builders) use it to test and refine prompt quality & model performance
• Enablement teams use it to identify where agents need training or support
• Ops leads use it to detect friction in workflows — before it shows up in CSAT
• Executives use it to align strategy with real adoption signals

And for the business overall, the gains are just as practical:

• Pinpoint where AI is helping — and where it’s being ignored
• Fine-tune deployments with behavioral feedback, not assumptions
• Close the loop between model, prompt, and agent action
• Prioritize where to invest next — based on what’s actually working
• Accelerate iteration across teams, without waiting for lagging metrics
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Quick Reminder: What the Score Actually Tells You

Before we close, here’s a simplified snapshot of what each score range means — and how teams can use it as a fast behavioral signal inside operations:

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Figure 4 : “Score-to-Behavior Reference” – a quick guide to interpreting agent trust levels by score range — from ignored suggestions to near-complete adoption.
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Unlike traditional KPIs, this score doesn’t just tell you what’s active — it shows you what’s actually being used.

And that’s the shift:

From tracking adoption, to managing confidence.

Because if you can’t see it, you can’t scale it.

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